#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import collections
import math
import os
import random
#from tempfile import gettempdir
#import zipfile

import numpy as np
from six.moves import urllib
from six.moves import xrange
import tensorflow as tf
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['simhei']
#plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['axes.unicode_minus'] = False

#step 1 指定文件并读取
filename = 'QuanSongCi.txt'

def read_data(filename):
	f = open(filename)
	#data = tf.compat.as_str(f.read()).split()
	data = [word for word in f.read()]
	return data

vocabulary = read_data(filename)

print('Data size ',len(vocabulary))

# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 5000

def build_dataset(words,n_words):
	count = [['UNK',-1]]
	count.extend(collections.Counter(words).most_common(n_words - 1))
	dictionary = dict()
	for word,_ in count:
		dictionary[word] = len(dictionary)
	data = list()
	unk_count = 0
	for word in words:
		index = dictionary.get(word,0)
		if index == 0:
			unk_count += 1
		data.append(index)
	count[0][1] = unk_count
	reversed_dictionary = dict(zip(dictionary.values(),dictionary.keys()))
	
	return data,count,dictionary,reversed_dictionary

data,count,dictionary,reverse_dictionary = build_dataset(vocabulary,vocabulary_size)

with open('dictionary.json','w') as of:
	json.dump(dictionary,of,ensure_ascii=False,indent=4)
with open('reverse_dictionary.json','w') as of:
	json.dump(reverse_dictionary,of,ensure_ascii=False,indent=4)

del vocabulary

print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0

# Step 3: Function to generate a training batch for the skip-gram model.

def generate_batch(batch_size, num_skips, skip_window):
	global data_index
	assert batch_size % num_skips == 0
	assert num_skips <= 2 * skip_window
	batch = np.ndarray(shape=(batch_size), dtype=np.int32)
	labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
	span = 2 * skip_window + 1  # [ skip_window target skip_window ]
	buffer = list()#collections.deque(maxlen=span)
	if data_index + span > len(data):
		data_index = 0
	buffer.extend(data[data_index:data_index + span])
	data_index += span
	for i in range(batch_size // num_skips):
		context_words = [w for w in range(span) if w != skip_window]
		words_to_use = random.sample(context_words, num_skips)
		for j, context_word in enumerate(words_to_use):
			batch[i * num_skips + j] = buffer[skip_window]
			labels[i * num_skips + j, 0] = buffer[context_word]
		if data_index == len(data):
			buffer[:] = data[:span]
			data_index = span
		else:
			buffer.append(data[data_index])
			data_index += 1
			if len(buffer) > span:
				buffer = buffer[1:]
	# Backtrack a little bit to avoid skipping words in the end of a batch
	data_index = (data_index + len(data) - span) % len(data)
	return batch, labels
	
batch,labels = generate_batch(batch_size=8,num_skips=2,skip_window=1)
for i in range(8):
	print(batch[i],reverse_dictionary[batch[i]],'->',labels[i,0],reverse_dictionary[labels[i,0]])

# Step 4: Build and train a skip-gram model.
batch_size = 256
embedding_size = 128  # Dimension of the embedding vector.
skip_window = 3       # How many words to consider left and right.
num_skips = 2         # How many times to reuse an input to generate a label.
num_sampled = 64      # Number of negative examples to sample.

valid_size = 16     # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)

graph = tf.Graph()

with graph.as_default():
	# Input data.
	train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
	train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
	valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

	# Ops and variables pinned to the CPU because of missing GPU implementation
	with tf.device('/cpu:0'):
		# Look up embeddings for inputs.
		embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
		embed = tf.nn.embedding_lookup(embeddings, train_inputs)

		# Construct the variables for the NCE loss
		nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size],
			stddev=1.0 / math.sqrt(embedding_size)))
		nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

  # Compute the average NCE loss for the batch.
  # tf.nce_loss automatically draws a new sample of the negative labels each
  # time we evaluate the loss.
  # Explanation of the meaning of NCE loss:
  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
	loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights,
                     biases=nce_biases,
                     labels=train_labels,
                     inputs=embed,
                     num_sampled=num_sampled,
                     num_classes=vocabulary_size))

  # Construct the SGD optimizer using a learning rate of 1.0.
	optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

  # Compute the cosine similarity between minibatch examples and all embeddings.
	norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
	normalized_embeddings = embeddings / norm
	valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)
	similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)

  # Add variable initializer.
	init = tf.global_variables_initializer()
	
num_steps = 400001

with tf.Session(graph=graph) as session:
	# We must initialize all variables before we use them.
	init.run()
	print('Initialized')
	
	average_loss = 0
	for step in xrange(num_steps):
		batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
		feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

    # We perform one update step by evaluating the optimizer op (including it
    # in the list of returned values for session.run()
		_, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
		average_loss += loss_val
		
		if step % 2000 == 0:
			if step > 0:
				average_loss /= 2000
      # The average loss is an estimate of the loss over the last 2000 batches.
			print('Average loss at step ', step, ': ', average_loss)
			average_loss = 0

    # Note that this is expensive (~20% slowdown if computed every 500 steps)
		if step % 10000 == 0:
			sim = similarity.eval()
			for i in xrange(valid_size):
				valid_word = reverse_dictionary[valid_examples[i]]
				top_k = 8  # number of nearest neighbors
				nearest = (-sim[i, :]).argsort()[1:top_k + 1]
				log_str = 'Nearest to %s:' % valid_word
				for k in xrange(top_k):
					close_word = reverse_dictionary[nearest[k]]
					log_str = '%s %s,' % (log_str, close_word)
				print(log_str)
	final_embeddings = normalized_embeddings.eval()
	np.save('embedding.npy',final_embeddings)

# Step 6: Visualize the embeddings.


# pylint: disable=missing-docstring
# Function to draw visualization of distance between embeddings.
def plot_with_labels(low_dim_embs, labels, filename):
	assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
	#myfont = matplotlib.font_manager.FontProperties(fname="STHeiti-Light.ttc")
	plt.figure(figsize=(18, 18))  # in inches
	for i, label in enumerate(labels):
		x, y = low_dim_embs[i, :]
		plt.scatter(x, y)
		plt.annotate(label,
				xy=(x, y),
				xytext=(5, 2),
				textcoords='offset points',
				ha='right',
				va='bottom')

	plt.savefig(filename)
	print('save image file.')
	
try:
    # pylint: disable=g-import-not-at-top
	from sklearn.manifold import TSNE
	import matplotlib.pyplot as plt
	
	tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
	plot_only = 500
	low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
	labels = [reverse_dictionary[i] for i in xrange(plot_only)]
	plot_with_labels(low_dim_embs, labels, '/home/chsl/ai/homework_week11/quiz-w11-code/tsne.png')

except ImportError as ex:
	print('Please install sklearn, matplotlib, and scipy to show embeddings.')
	print(ex)